Christensen, Peter
[VerfasserIn];
Francisco, Paul
[VerfasserIn];
Myers, Erica
[VerfasserIn];
Shao, Hansen
[VerfasserIn];
Souza, Mateus
[VerfasserIn]
;
National Bureau of Economic Research
Energy Efficiency Can Deliver for Climate Policy
:
Evidence from Machine Learning-Based Targeting
Beschreibung:
Building energy efficiency has been a cornerstone of greenhouse gas mitigation strategies for decades. However, impact evaluations have revealed that energy savings typically fall short of engineering model forecasts that currently guide funding decisions. This creates a resource allocation problem that impedes progress on climate change. Using data from the largest U.S. energy efficiency program, we demonstrate that a data-driven approach to predicting retrofit impacts based on previously realized outcomes is more accurate than the status quo engineering models. Targeting high-return interventions based on these predictions dramatically increases net social benefits, from $0.93 to $1.23 per dollar invested